{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOddl3p3PRsCszWtVsAJOO4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/svetakvsundhar/professional-services/blob/main/colab_notebooks/beam_to_storm.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Welcome to the Beam-to-Storm Colab notebook!**\n",
        "\n",
        "This notebook will allow you to get an initial glimpse of how Apache Beam Code may be represented in terms of Apache Storm constructs. Note that this tool simply provides a template, and no guarantees that the Gemini output is runnable/production ready."
      ],
      "metadata": {
        "id": "vCfGpZzbiJ03"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BDD1hnKxgZMH",
        "outputId": "e71af5f8-dd7c-4c95-f4f6-277fa2189dc6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m164.2/164.2 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m718.3/718.3 kB\u001b[0m \u001b[31m14.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q -U google-generativeai"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import google.generativeai as genai"
      ],
      "metadata": {
        "id": "bXbUF00Ogdio"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Configure your API key**\n",
        "\n",
        "To run the following cell, your API key must be stored it in a Colab Secret named GOOGLE_API_KEY. If you don't already have an API key, or you're not sure how to create a Colab Secret, see Authentication for an example."
      ],
      "metadata": {
        "id": "VRty7oWwiZgR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import userdata\n",
        "GOOGLE_API_KEY=userdata.get('GOOGLE_API_KEY')\n",
        "\n",
        "genai.configure(api_key=GOOGLE_API_KEY)"
      ],
      "metadata": {
        "id": "u-CE4-Aiglc0"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Upload your file onto this Colab Notebook (note, please use a .txt file). Then, replace **path_to_your_file.txt** with your actual path."
      ],
      "metadata": {
        "id": "JKjVyiSjieX1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "your_file = genai.upload_file(path='#path_to_your_file.txt')"
      ],
      "metadata": {
        "id": "CnKJTDyygpAH"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "your_architecture_description=\"This is an Apache Beam pipeline being executed on Dataflow Runner\""
      ],
      "metadata": {
        "id": "iAiHKFK3gw4Z"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "design_img = \"Gemini, your role is to convert this Apache Beam code into Apache Storm code,from the code provided and the description. Please return Apache Storm code.\""
      ],
      "metadata": {
        "id": "85xu5QWZgzRZ"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = genai.GenerativeModel('models/gemini-1.5-pro-latest')\n",
        "prompt = model.generate_content(your_architecture_description + design_img)\n",
        "response = model.generate_content([prompt.text, your_file])\n",
        "print(response.text)"
      ],
      "metadata": {
        "id": "8YkEn_rKg8eb"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}